Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Cancer type and histology influence cutaneous immunotherapy toxicities: a multi-institutional cohort study.

BACKGROUND: Cutaneous immune-related adverse events (cirAEs) are the most common toxicities to occur in the setting of immune checkpoint inhibitor (ICI) therapy. Identifying patients who are at increased risk of developing cirAEs may improve quality of life and outcomes. OBJECTIVES: To investigate the influence of cancer type and histology on the development of cirAEs in the setting of ICI therapy and survival outcomes. METHODS: This retrospective cohort study included patients recruited between 1 December 2011 and 30 October 2020. They received ICI from 2011 to 2020 with follow-up of outcomes through October 2021. We identified 3668 recipients of ICI therapy who were seen at Massachusetts General Brigham and Dana-Farber. Of these, 669 developed cirAEs. Records that were incomplete or categories of insufficient sample size were excluded from the study cohort. Multivariate Cox proportional hazards models were used to investigate the impact of cancer organ system and histology on cirAE development, after adjusting for demographics, Charlson Comorbidity Index, ICI type, cancer stage at ICI initiation, and year of ICI initiation. Time-varying Cox proportional hazards modelling was used to examine the impact of cirAE development on mortality. RESULTS: Compared with other nonepithelial cancers (neuroendocrine, leukaemia, lymphoma, myeloma, sarcoma and central nervous system malignancies), cutaneous squamous cell carcinoma [cSCC; hazard ratio (HR) 3.57, P < 0.001], melanoma (HR 2.09, P < 0.001), head and neck adenocarcinoma (HR 2.13, P = 0.009), genitourinary transitional cell carcinoma (HR 2.15, P < 0.001) and genitourinary adenocarcinoma (HR 1.53, P = 0.037) were at significantly higher risk of cirAEs in multivariate analyses. The increased risk of cirAEs translated into an adjusted survival benefit for melanoma (HR 0.37, P < 0.001) and cSCC (HR 0.51, P = 0.011). CONCLUSIONS: The highest rate of cirAEs and subsequent survival benefits were observed in cutaneous malignancies treated with ICI therapies. This study improves our understanding of patients who are at highest risk of developing cirAEs and would, therefore, benefit from appropriate counselling and closer monitoring by their oncologists and dermatologists throughout their ICI therapy. Limitations include its retrospective nature and cohort from one geography.

Cutaneous immune-related adverse events (cirAEs) are the most common complications to occur for oncology patients treated with immune checkpoint inhibitors (ICIs). cirAEs can lead to increased use of healthcare resources and significant morbidity. Identifying patients who are at increased risk of developing cirAEs may improve quality of life and outcomes. In this study, we aimed to investigate the influence of cancer organ system and histology on the development of cirAEs and survival outcomes. To do this, we included a cohort of patients retrospectively between 1 December 2011 and 30 October 2020. We identified 3668 ICI recipients who were seen at Massachusetts General Brigham and Dana-Farber in Boston, Massachusetts. Of these, 669 people developed cirAEs. Multivariate Cox proportional hazards models were used to investigate the impact of cancer organ system and histology on cirAE development, after adjusting for demographics, Charlson Comorbidity Index, ICI type, cancer stage at ICI start, and year of ICI initiation. Time-varying Cox proportional hazards modelling was used to examine the impact of cirAE development on mortality. We found that, compared with other nonepithelial cancers, patients with cutaneous squamous cell carcinoma (cSCC) and melanoma were at significantly higher risk of cirAEs. The increased risk of cirAEs translated into an adjusted survival benefit for melanoma and cSCC. This study improves our understanding of patients who are at highest risk of developing cirAEs ­ those with melanoma and cSCC. Therefore, many patients could benefit from appropriate counselling and close monitoring by their oncologists and dermatologists throughout ICI therapy.

Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.

BACKGROUND: The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality. OBJECTIVE: To develop time-to-event risk prediction models for melanoma metastatic recurrence. METHODS: Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction. RESULTS: This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. LIMITATIONS: Retrospective nature and cohort from one geography. CONCLUSIONS: These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.

Large-scale real-world data analyses of cancer risks among patients with rheumatoid arthritis.

Rheumatoid arthritis (RA) affects 24.5 million people worldwide and has been associated with increased cancer risks. However, the extent to which the observed risks are related to the pathophysiology of rheumatoid arthritis or its treatments is unknown. Leveraging nationwide health insurance claims data with 85.97 million enrollees across 8 years, we identified 92 864 patients without cancers at the time of rheumatoid arthritis diagnoses. We matched 68 415 of these patients with participants without rheumatoid arthritis by sex, race, age and inferred health and economic status and compared their risks of developing all cancer types. By 12 months after the diagnosis of rheumatoid arthritis, rheumatoid arthritis patients were 1.21 (95% confidence interval [CI] [1.14, 1.29]) times more likely to develop any cancer compared with matched enrollees without rheumatoid arthritis. In particular, the risk of developing lymphoma is 2.08 (95% CI [1.67, 2.58]) times higher in the rheumatoid arthritis group, and the risk of developing lung cancer is 1.69 (95% CI [1.32, 2.13]) times higher. We further identified the five most commonly used drugs in treating rheumatoid arthritis, and the log-rank test showed none of them is implicated with a significantly increased cancer risk compared with rheumatoid arthritis patients without that specific drug. Our study suggested that the pathophysiology of rheumatoid arthritis, rather than its treatments, is implicated in the development of subsequent cancers. Our method is extensible to investigating the connections among drugs, diseases and comorbidities at scale.

Cutaneous immune-related adverse events are associated with longer overall survival in advanced cancer patients on immune checkpoint inhibitors: A multi-institutional cohort study.

BACKGROUND: Cutaneous immune-related adverse events (cirAEs) occur in up to 40% of immune checkpoint inhibitor (ICI) recipients. However, the association of cirAEs with survival remains unclear. OBJECTIVE: To investigate the association of cirAEs with survival among ICI recipients. METHODS: ICI recipients were identified from the Mass General Brigham healthcare system and Dana-Farber Cancer Institute. Patient charts were reviewed for cirAE development within 2 years after ICI initiation. Multivariate time-varying Cox proportional hazards models, adjusted for age, sex, race/ethnicity, Charlson Comorbidity Index, ICI type, cancer type, and year of ICI initiation were utilized to investigate the impact of cirAE development on overall survival. RESULTS: Of the 3731 ICI recipients, 18.1% developed a cirAE. Six-month landmark analysis and time-varying Cox proportional hazards models demonstrated that patients who developed cirAEs were associated with decreased mortality (hazardratio [HR] = 0.87, P = .027), particularly in patients with melanoma (HR = 0.67, P = .003). Among individual morphologies, lichenoid eruption (HR = 0.51, P < .001), psoriasiform eruption (HR = 0.52, P = .005), vitiligo (HR = 0.29, P = .007), isolated pruritus without visible manifestation of rash (HR = 0.71, P = .007), acneiform eruption (HR = 0.34, P = .025), and non-specific rash (HR = 0.68, P < .001) were significantly associated with better survival after multiple comparisons adjustment. LIMITATIONS: Retrospective design; single geography. CONCLUSION: CirAE development is associated with improved survival among ICI recipients, especially patients with melanoma.

Influence of melanoma type on incidence and downstream implications of cutaneous immune-related adverse events in the setting of immune checkpoint inhibitor therapy.

BACKGROUND: Emerging evidence suggests that cutaneous immune-related adverse events (cirAEs) are associated with a survival benefit in the setting of advanced melanoma treated with immune checkpoint inhibitor (ICI) therapy. Previous studies have not examined the role of melanoma subtypes on cirAE development and downstream therapeutic outcomes. OBJECTIVE: Examine the impact of melanoma subtypes on cirAE onset and survival among ICI recipients. METHODS: Retrospective multi-institutional cohort study. Multivariate time-series regressions were utilized to assess relationships between melanoma subtype, cirAE development, and survival. RESULTS: Among 747 ICI recipients, 236 (31.6%) patients developed a cirAE. Patients with acral melanoma were less likely to develop a cirAE (hazard ratio [HR] = 0.41, P = .016) compared to patients with nonacral cutaneous melanoma. Across all melanoma subtypes, cirAEs were associated with reduced mortality (HR = 0.76, P = .042). Patients with acral (HR = 2.04, P = .005), mucosal (HR = 2.30, P < .001), and uveal (HR = 4.09, P < .001) primaries exhibited the worst survival. LIMITATIONS: Retrospective cohort study. CONCLUSION: This is the first study to demonstrate differences in cirAE development among melanoma subtypes. The presence of cirAEs was associated with better survival. Further, the lower incidence of cirAEs may be a marker of immunotherapy response, which is reflected in the association between acral melanoma and mortality.

Epidemiology and risk factors for the development of cutaneous toxicities in patients treated with immune-checkpoint inhibitors: A United States population-level analysis.

BACKGROUND: A variety of dermatoses have been reported in the growing number of patients treated with immune-checkpoint inhibitors (ICIs), but the current understanding of cutaneous immune-related adverse events (irAEs) is limited. OBJECTIVE: To determine the cumulative incidence, distribution, and risk factors of cutaneous irAEs after ICI initiation. METHODS: This was a retrospective cohort study of patients in a national insurance claims database including cancer patients treated with ICIs and matched controls. RESULTS: The study included 8637 ICI patients and 8637 matched controls. The overall incidence of cutaneous irAEs was 25.1%, with a median onset time of 113 days. The ICI group had a significantly higher incidence of pruritus, mucositis, erythroderma, maculopapular eruption, vitiligo, lichen planus, bullous pemphigoid, Grover disease, rash, other nonspecific eruptions, and drug eruption or other nonspecific drug reaction. Patients with melanoma and renal cell carcinoma and those receiving combination therapy were at a higher risk of cutaneous irAEs. LIMITATIONS: Retrospective design without access to patient chart data. CONCLUSIONS: This study identifies cutaneous irAEs in a real-world clinical setting and highlights patient groups that are particularly at risk. The results can aid dermatologists at the bedside in the diagnosis of cutaneous irAEs and in formulating management recommendations to referring oncologists regarding the continuation of ICI therapy.

Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks.

BACKGROUND: Ovarian cancer causes 151,900 deaths per year worldwide. Treatment and prognosis are primarily determined by the histopathologic interpretation in combination with molecular diagnosis. However, the relationship between histopathology patterns and molecular alterations is not fully understood, and it is difficult to predict patients' chemotherapy response using the known clinical and histological variables. METHODS: We analyzed the whole-slide histopathology images, RNA-Seq, and proteomics data from 587 primary serous ovarian adenocarcinoma patients and developed a systematic algorithm to integrate histopathology and functional omics findings and to predict patients' response to platinum-based chemotherapy. RESULTS: Our convolutional neural networks identified the cancerous regions with areas under the receiver operating characteristic curve (AUCs) > 0.95 and classified tumor grade with AUCs > 0.80. Functional omics analysis revealed that expression levels of proteins participated in innate immune responses and catabolic pathways are associated with tumor grade. Quantitative histopathology analysis successfully stratified patients with different response to platinum-based chemotherapy (P = 0.003). CONCLUSIONS: These results indicated the potential clinical utility of quantitative histopathology evaluation in tumor cell detection and chemotherapy response prediction. The developed algorithm is easily extensible to other tumor types and treatment modalities.

Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation.

BACKGROUND: Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely compared or reproduced. OBJECTIVE: The goal of the research was to generate reproducible machine learning modules for lung cancer detection and compare the approaches and performances of the award-winning algorithms developed in the Kaggle Data Science Bowl. METHODS: We obtained the source codes of all award-winning solutions of the Kaggle Data Science Bowl Challenge, where participants developed automated CT evaluation methods to detect lung cancer (training set n=1397, public test set n=198, final test set n=506). The performance of the algorithms was evaluated by the log-loss function, and the Spearman correlation coefficient of the performance in the public and final test sets was computed. RESULTS: Most solutions implemented distinct image preprocessing, segmentation, and classification modules. Variants of U-Net, VGGNet, and residual net were commonly used in nodule segmentation, and transfer learning was used in most of the classification algorithms. Substantial performance variations in the public and final test sets were observed (Spearman correlation coefficient = .39 among the top 10 teams). To ensure the reproducibility of results, we generated a Docker container for each of the top solutions. CONCLUSIONS: We compared the award-winning algorithms for lung cancer detection and generated reproducible Docker images for the top solutions. Although convolutional neural networks achieved decent accuracy, there is plenty of room for improvement regarding model generalizability.

Evaluation of the association of bariatric surgery with subsequent depression.

BACKGROUND/OBJECTIVES: Bariatric surgery is helpful in enabling sustained weight loss, but effects on depression are unclear. Reductions in depression-related symptoms and increases in suicide rate have both been observed after bariatric surgery, but these observations are confounded by the presence of pre-existing depression. The goal of this study is to evaluate the effect of bariatric surgery on subsequent depression diagnosis. SUBJECTS/METHODS: In this observational study, a prospective cohort study was simulated by evaluating depression risk based on diagnostic codes. An administrative database was utilized for this study, containing records and observations between 1 January 2008 through 29 February 2016 of enrolled patients in the United States. Individuals considered in this analysis were enrolled in a commercial health insurance program, observed for at least 6 months prior to surgery, and met the eligibility criteria for bariatric surgery. In all, 777,140 individuals were considered in total. RESULTS: Bariatric surgery was found to be significantly associated with subsequent depression relative to both non-surgery controls (HR = 1.31, 95% CI, 1.27-1.34, P < 2e-32) and non-bariatric abdominal surgery controls (HR = 2.15, 95% CI, 2.09-2.22, P < 2e-32). Patients with pre-surgical psychiatric screening had a reduced depression hazard ratio with respect to patients without (HR = 0.85, 95% CI, 0.81-0.89, P = 3.208e-12). Men were found to be more susceptible to post-bariatric surgery depression compared with women. Pre-surgical psychiatric evaluations reduced the magnitude of this effect. Relative to bariatric surgeries as a whole, vertical sleeve gastrectomy had a lower incidence of depression, while Roux-en Y Gastric Bypass and revision/removal surgeries had higher rates. CONCLUSIONS: In individuals without a history of depression, bariatric surgery is associated with subsequent diagnosis of depression. This study provides guidance for patients considering bariatric surgery and their clinicians in terms of evaluating potential risks and benefits of surgery.

Proceedings of the fourth international molecular pathological epidemiology (MPE) meeting.

An important premise of epidemiology is that individuals with the same disease share similar underlying etiologies and clinical outcomes. In the past few decades, our knowledge of disease pathogenesis has improved, and disease classification systems have evolved to the point where no complex disease processes are considered homogenous. As a result, pathology and epidemiology have been integrated into the single, unified field of molecular pathological epidemiology (MPE). Advancing integrative molecular and population-level health sciences and addressing the unique research challenges specific to the field of MPE necessitates assembling experts in diverse fields, including epidemiology, pathology, biostatistics, computational biology, bioinformatics, genomics, immunology, and nutritional and environmental sciences. Integrating these seemingly divergent fields can lead to a greater understanding of pathogenic processes. The International MPE Meeting Series fosters discussion that addresses the specific research questions and challenges in this emerging field. The purpose of the meeting series is to: discuss novel methods to integrate pathology and epidemiology; discuss studies that provide pathogenic insights into population impact; and educate next-generation scientists. Herein, we share the proceedings of the Fourth International MPE Meeting, held in Boston, MA, USA, on 30 May-1 June, 2018. Major themes of this meeting included 'integrated genetic and molecular pathologic epidemiology', 'immunology-MPE', and 'novel disease phenotyping'. The key priority areas for future research identified by meeting attendees included integration of tumor immunology and cancer disparities into epidemiologic studies, further collaboration between computational and population-level scientists to gain new insight on exposure-disease associations, and future pooling projects of studies with comparable data.

Omics AnalySIs System for PRecision Oncology (OASISPRO): a web-based omics analysis tool for clinical phenotype prediction.

SUMMARY: Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general purpose data mining tool exists for physicians, medical researchers and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. AVAILABILITY AND IMPLEMENTATION: This web-based tool is available at http://tinyurl.com/oasispro; source codes are available at http://tinyurl.com/oasisproSourceCode. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining.

There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine.

A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project.

Targeted metabolomics and biochemical studies complement the ongoing investigations led by the Human Proteome Organization (HUPO) Biology/Disease-Driven Human Proteome Project (B/D-HPP). However, it is challenging to identify and prioritize metabolite and chemical targets. Literature-mining-based approaches have been proposed for target proteomics studies, but text mining methods for metabolite and chemical prioritization are hindered by a large number of synonyms and nonstandardized names of each entity. In this study, we developed a cloud-based literature mining and summarization platform that maps metabolites and chemicals in the literature to unique identifiers and summarizes the copublication trends of metabolites/chemicals and B/D-HPP topics using Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores. We successfully prioritized metabolites and chemicals associated with the B/D-HPP targeted fields and validated the results by checking against expert-curated associations and enrichment analyses. Compared with existing algorithms, our system achieved better precision and recall in retrieving chemicals related to B/D-HPP focused areas. Our cloud-based platform enables queries on all biological terms in multiple species, which will contribute to B/D-HPP and targeted metabolomics/chemical studies.

Omics Profiling in Precision Oncology.

Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology.

Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures.

Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P < 0.0001). We further built a least absolute shrinkage and selection operator (LASSO)-Cox proportional hazards model that stratified patients into early relapse and late relapse groups (P = 0.00013). The top proteomic features indicative of platinum response were involved in ATP synthesis pathways and Ran GTPase binding. Overall, we demonstrated that proteomic profiles of ovarian serous carcinoma patients predicted platinum drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers.

The genetic predisposition to bronchopulmonary dysplasia.

PURPOSE OF REVIEW: Bronchopulmonary dysplasia (BPD) is a prevalent chronic lung disease in premature infants. Twin studies have shown strong heritability underlying this disease; however, the genetic architecture of BPD remains unclear. RECENT FINDINGS: A number of studies employed different approaches to characterize the genetic aberrations associated with BPD, including candidate gene studies, genome-wide association studies, exome sequencing, integrative omics analysis, and pathway analysis. Candidate gene studies identified a number of genes potentially involved with the development of BPD, but the etiological contribution from each gene is not substantial. Copy number variation studies and three independent genome-wide association studies did not identify genetic variations significantly and consistently associated with BPD. A recent exome-sequencing study pointed to rare variants implicated in the disease. In this review, we summarize these studies' methodology and findings, and suggest future research directions to better understand the genetic underpinnings of this potentially life-long lung disease. SUMMARY: Genetic factors play a significant role in the development of BPD. Recent studies suggested that rare variants in genes participating in lung development pathways could contribute to BPD susceptibility.